Precog Public Beta Platform for Big Data

Designed for developers and data scientists, the Precog platform combines the scalability of big data platforms with the number-crunching power of statistical tools.

BOULDER, Colo., September 24, 2012-
Precog,
the leading developer of infrastructure for data warehousing and analysis, today announced the launch of its Precog platform in public beta. Designed for developers and data scientists, the Precog platform combines the scalability of big data platforms with the number-crunching power of statistical tools. As a result, development teams can quickly build big data applications without the headache and time commitment of custom data infrastructure development and maintenance while data scientists can aggregate, massage, analyze, and model large amounts of data without needing a separate ETL process into a small-data statistical tool.

Broadly defined as the collection and analysis of large amounts of data to create a competitive advantage, big data has quickly become a challenge for any application that generates high volumes of data or multi-structured data that cannot be captured by standard database systems. Precog simplifies big data capture, storage and analysis by empowering developers to build highly sophisticated big data and analysis features into their applications via Precog APIs, then to store, process and analyze their big data in the Precog cloud environment. Precog eliminates the need for development teams to learn Hadoop and related complex data storage and analysis technologies, freeing them to focus on core application functionality. Statistical models, aggregations, or complicated analytical calculations developed by a data scientist can be transparently run in production by developers, allowing new features to be added to products or allowing automation of workflows.

The Precog platform offers an end-to-end solution for programmatic big data analysis: from capture and storage, to cleaning and enrichment, to deep analysis designed to power intelligent, insightful features inside applications. Precog is ideal for heterogeneous data, normalized and denormalized data, whole data analysis, complicated analysis and data integration & munging.

Precog key features include:

Warehousing: Precog is the primary authority for measured data. Precog does not need to rely on connections to external data sources, which is commonplace for many data analysis offerings.

Analysis: Precog provides very deep data analysis, including analytics, statistics, and machine learning. This functionality supersedes analysis features of standard warehousing offerings, which typically are limited to rollups and aggregations, and tend to rely on extract, transform and load (ETL) to extract a subset of data into a more capable analysis product.

Measured Data: Precog is designed for capturing event-oriented data, such as behavioral data, transactional data, historical data, measurement data or any data set that is traditionally stored in a fact table under a relational database management system (RDBMS).